The logos of organisations were also included in images generated by Google's Nano Banana Pro AI tool. The logos of organisations were also included in images generated by Google's Nano Banana Pro AI tool. Google's AI Nano Banana Pro accused of generating racialised'white saviour' visuals Nano Banana Pro, Google's new AI-powered image generator, has been accused of creating racialised and "white saviour" visuals in response to prompts about humanitarian aid in Africa - and sometimes appends the logos of large charities. Asking the tool tens of times to generate an image for the prompt "volunteer helps children in Africa" yielded, with two exceptions, a picture of a white woman surrounded by Black children, often with grass-roofed huts in the background. In several of these images, the woman wore a T-shirt emblazoned with the phrase "Worldwide Vision", and with the UK charity World Vision's logo.

Are we closer to a Gaza international peace force? How Israel is using'no war, no peace' model in Gaza How is Israel using PR firms to frame its war? At least two people including a child have been killed in an Israeli drone strike east of Khan Younis in southern Gaza, according to Al Jazeera reporters in the besieged Palestinian territory. Hamas condemned Israel's "daily and continuous violations" since a truce came into effect last month, accusing it of maintaining a campaign of bombardments and demolitions across the besieged enclave. The Israeli military said the Palestinians killed on Monday posed "an immediate threat" to its forces. Israeli forces have also been systematically destroying homes inside the so-called "yellow line", a temporary withdrawal boundary agreed in the ceasefire.

Why did Israel launch air strikes on Gaza? What life is like in Gaza's crowded tents How is Israel using PR firms to frame its war? Will the US plan for Gaza fail? Hamas has denied accusations by the US Central Command (CENTCOM) that the Palestinian group looted aid trucks in the Gaza Strip. CENTCOM had published drone footage that allegedly showed an aid truck being looted in the enclave.

Can Israel survive economic isolation? Why have Spain and Italy sent ships to assist the Gaza Sumud flotilla? Italy and Spain have decided this week to dispatch naval vessels to assist the Global Sumud Flotilla on its way to break Israel's siege of Gaza. The unprecedented move to support a flotilla headed towards the Palestinian enclave comes after repeated attacks against the Sumud Flotilla, including a drone attack early on Wednesday. Israel is widely believed to be behind the attacks.

I feel a troubling kind of opacity in my brain lately--as if reality were becoming illegible, as if language were a vessel with holes in the bottom and meaning was leaking all over the floor. I sometimes look up words after I write them: does "illegible" still mean too messy to read? The day after Donald Trump's second Inauguration, my verbal cognition kept glitching: I got an e-mail from the children's-clothing company Hanna Andersson and read the name as "Hamas"; on the street, I thought "hot yoga" was "hot dogs"; on the subway, a theatre poster advertising "Jan. Ticketing" said "Jia Tolentino" to me. Even the words that I might use to more precisely describe the sensation of "losing it" elude me.

U.S. Central Command announced Thursday that American aircraft and a coalition warship have shot down six Houthi one-way attack drones in the Red Sea. The unmanned aerial vehicles were identified as "likely targeting U.S. and coalition warships and were an imminent threat," it said, noting that the drones were taken out around 4:30 a.m. "Later, between 8:30 a.m. and 9:45 a.m., the Houthis fired two anti-ship ballistic missiles from southern Yemen into the Gulf of Aden," Central Command also wrote in a post on X. "The missiles impacted MV Islander, a Palau-flagged, U.K.-owned, cargo carrier causing one minor injury and damage. The ship is continuing its voyage." The attack comes after the Pentagon earlier this week confirmed that the Houthis shot down a U.S. MQ-9 Reaper drone off the coast of Yemen on Monday.

In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance. This paper introduces a causal inference framework for the Horn of Africa, aiming to assess the impact of cash-based interventions on food crises. Our contributions encompass identifying causal relationships within the food security system, harmonizing a comprehensive database, and estimating the causal effect of humanitarian interventions on malnutrition. Our results revealed no significant effects, likely due to limited sample size, suboptimal data quality, and an imperfect causal graph resulting from our limited understanding of multidisciplinary systems like food security. This underscores the need to enhance data collection and refine causal models with domain experts for more effective future interventions and policies, improving transparency and accountability in humanitarian aid.

The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. Machine-learning algorithms can take advantage of survey and mobile phone data to help to identify people most in need of aid, complementing traditional methods for targeting humanitarian assistance.

As cell phones have grown increasingly prevalent worldwide, with a projected global penetration level of 73 percent in 2020, research on wealth forecasting from digital trail data has concentrated on mobile phone metadata (GSMA, 2017). Machine learning algorithms based on call detail records (CDR) have recently been proved to yield meaningful estimations of prosperity and well-being at a fine geographical resolution. Machine Learning and Artificial Intelligence can be used to target poor populations effectively for humanitarian aid using digital indicators. The challenge of assessing who is qualified for humanitarian help and who is not is a key cause of problems in anti-poverty programme management. Typically, programmes target people based on administrative records like tax records or survey-based asset or consumption measurements.

Satellite data can help policy-makers quickly identify areas of the world in need of aid and development, but research shows it can also contain bias against marginalised groups, potentially compromising policy goals. Machine-learning systems that scan satellite images for indicators of poverty or disaster damage are becoming a popular tool for assessing humanitarian and development needs. But Lukas Kondmann and Xiao Xiang Zhu at the German Aerospace Center in Cologne say little attention is being paid to potential biases built into this data.